Scientific publications by our experts

Our experts contribute to the growth of the research community. Here is the list of the scientific publications they were involved in.

Scientific publications and presentations

2019

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

(in press) Nath, V. Schilling, K. G., et int. (…, Rowe, M., Rodrigues, P., Prčkovska, V., …), & Landman, B. A. (2019). “Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge”. Journal of Magnetic Resonance Imaging.

This paper details the competition organised by Bennett Landmann and held at the diffusion study group at ISMRM 2017 in which a number of teams competed to produce the most reproducible tractography on a set of specified white matter structures. QMENTA’s entry was the 3rd most reproducible method.

Advanced medical imaging handling and analysis in clinical trials

Rowe, M. (2019). Advanced medical imaging handling and analysis in clinical trials. In Alzheimer’s & Parkinson’s Diseases Congress – AD/PD Lisbon, 2019.

Presentation on the acceleration of handling imaging data in clinical trials through a cloud-based centralized data repository and management system.

Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation

Puch, S., Sánchez, I., Hernández, A., Piella, G., Prc̆kovska, V. (2019). Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation. In: Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M., van Walsum T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science, vol 11384. Springer, Cham.

In this paper, we introduce the Global Planar Convolution module, a building-block for Convolutional Neural Networks that enhances context perception capabilities, and we apply them to the task of delineating gliomas and their main compartments.

Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation with MR images

Puch, S.Sánchez, I., Hernández, A., Piella, G., Rodrigues, P., & Prčkovska, V. (2019). Global planar convolutions for improved context aggregation in brain tumor segmentation with MR images. In ISMRM 2019: 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

This publication describes our preliminary work on a novel architecture based on Global Planar Convolutions applied to the task of brain tumor segmentation.

Semi-automatic cloud-based workflow for evaluating the central vein sign for MS diagnosis in a multicenter clinical setting

Moreno-Dominguez, D., Ramos, M., Reich, D. S., Ontaneda, D., Rodrigues, P., & Sati, P. (2019). Semi-automatic cloud-based workflow for evaluating the central vein sign for MS diagnosis in a multicenter clinical setting. In ISMRM 2019: 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

In this work, we developed a semi-automatic cloud-based workflow for evaluating the clinical value of the central vein sign for MS diagnosis using FLAIR* in a multicenter setting. This novel workflow is a powerful tool that has the potential to significantly accelerate clinical research imaging studies in MS.

Automated cloud-based workflow for quantification of MRI signal intensity – initial real-world clinical validation

Ramos, M., Prčkovska, V., Rodrigues, P., Wang, J., Moser, F., Blank, M., Agarwal, S., Agris, J., & Moreno-Dominguez, D. (2019). Automated cloud-based workflow for quantification of MRI signal intensity – initial real-world clinical validation. In ISMRM 2019: 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

We present a fully automatic workflow which accelerates the investigation of contrast agent depositions such as Gadolinium by extracting the T1-weighted modal intensity value and applies appropriate corrections and normalizations to allow comparison across acquisitions and protocols. Automatic results matched up to 94% correlation with manual results and reduced the time by 90%.

Analysis of feature importance in deep neural networks in psychiatric disorders using magnetic resonance imaging

Sánchez, I., Soriano-Mas, C., Verdejo-García, A., Cardoner, N., Fernández-Aranda, F., Menchón, J. M., Rodrigues, P., Prčkovska, V., & Rowe, M. (2019). Analysis of feature importance in deep neural networks in psychiatric disorders using magnetic resonance imaging. In ISMRM 2019: 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

In this work, we train a neural network to differentiate between healthy subjects and patients of six different mental illnesses and we analyzed the network weights of the model to identify the most important regions of the brain for classification.

Reproducibility of SIENAX volumetric outputs over intra-session, inter-session and inter-scanner acquisitions

García, G.Moreno-Dominguez, D., Rowe, M., Prčkovska, V., & Rodrigues, P. (2019). Reproducibility of SIENAX volumetric outputs over intra-session, inter-session and inter-scanner acquisitions. In ISMRM 2019: 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

We conducted a reliability analysis for SIENAX in a test-retest dataset and a multi-site dataset. The volumetric outputs of SIENAX show low coefficients of variance for the test-retest dataset but quite higher multi-site data, suggesting a possible need for data harmonization in multi-site studies.

2018

Automated signal intensity quantification software – initial real world clinical validation

Wang, J., Moser, F., Moreno-Dominguez, D., Ramos, M., Prčkovska, V., Rodrigues, P., Markus Blank, S., & Agarwal, J. A. (2018). Automated signal intensity quantification software – initial “real world” clinical validation. Presentation at Western Neuroradiological Society 49th Annual Meeting.

Extensive research is now underway in trying to understand the mechanism of increased signal intensity (SI) and gadolinium presence in the brain and whether it has a clinical implication for patients. in this work, automated signal intensity quantification software was developed and validated in patient data from clinical routine.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Bakas, S., Reyes, M., et int. (…,  Puch, S., Sánchez, I., Prc̆kovska, V., …), & Menze, B. (2018). Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in MRI scans during the last instances of the Brain Tumor Segmentation (BraTS) challenge.

Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks.

Kazancli, E., Prchkovska, V., Rodrigues, P., Villoslada, P., & Igual, L. (2018). Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 260–269).

This publication evaluates novel approaches to segment Multiple Sclerosis lesions from MR scans based on Convolutional Neural Networks.

Cross-vendor and Cross-protocol harmonisation of diffusion MRI data: a comparative study

Tax, C. M. W., Grussu, F., et int. (…, Puch, S., Rowe, M., Rodrigues, P., Prčkovska, V., …) &Veraart, J. (2018). Cross-vendor and Cross-protocol harmonization of diffusion {MRI} data: a comparative study. In ISMRM 2018: 26th Annual Meeting of the International Society for Magnetic Resonance in Medicine (p. 471).

This paper presents the diffusion MRI harmonisation challenge held at MICCAI 2017, in which five different methods that estimate mappings between scanners for diffusion MRI data harmonisation were evaluated on a dedicated dataset of the same subjects acquired on three distinct scanners.

Classification of subjects with psychiatric disorders using Deep Learning and identification of relevant features in the data

Sánchez, I. (2018). Classification of subjects with disorders using Deep Learning and identification of relevant features in the data. Universitat Politècnica de Catalunya.

In this Thesis, a deep neural network was trained to classify between healthy control subjects and subjects suffering from six different mental illnesses. By pre-processing T1 images and rs-fMRI, morphological changes in terms of the volume of brain regions, and changes in functionality between these regions were used as input data. Using the trained weights of the model and a novel visualization tool implemented during the course of the Thesis, it was studied which regions of the brain can be used as potential biomarkers for improving the diagnosis of brain disorders.

Multimodal brain tumor segmentation in Magnetic Resonance Images with Deep Architectures

Puch, S. (2018). Multimodal brain tumor segmentation in Magnetic Resonance Images with Deep Architectures. Universitat Autònoma de Barcelona.

In this thesis, we evaluate multiple 3D convolutional neural networks on the task of brain tumor segmentation. We focus on context-aware and efficient architectures, and we train these architectures in two large datasets, the Brain Tumor Segmentation Challenge (BraTS) dataset and the QMENTA
Brain Tumors dataset.

2017

ISMRM 2017: TraCED Challenge Entry

Rowe, M., Rodrigues, P., & Prčkovska, V. (2017). ISMRM 2017: TraCED Challenge Entry – 3rd place. In ISMRM 2017: TraCED Challenge (pp. 2–4).

In this work, we present a methodology for automatically segmenting multiple white matter fiber structures using a combination of white matter ROIs and T1-weighted-derived gray matter parcellation. The automated method segments the fascicle structures defined in the challenge without any manual intervention.

MultipleMS: A distributed workflow for managing and processing neuroimaging data

Peeters, T.H.J.M., Lazovski, N., Puch, S., Alkin, A., Moreno-Dominguez, D., & Prčkovska, V. (2017). MultipleMS: A distributed workflow for managing and processing neuroimaging data.

In this whitepaper, we describe one such neuroscience project and we present our solution at QMENTA implemented together with the UCSF School of Medicine, Department of Neurology. In the end, we summarize the benefits of our approach and inform you on how you can also use our platform with minimum effort.

Data security on QMENTA cloud: security frameworks on the platform.

Sato, T., Alkin, A., Lazovski, N., & Rodrigues, P. (2017). Data security on QMENTA cloud: security frameworks on the platform.

QMENTA understands the security requirements of the cloud architecture and the platform is designed to deliver even better security than traditional on-premises data archiving systems. Our comprehensive security strategy includes technical implementation, organizational structure, and many other business operations. The client’s data is protected at all times – whether it is travelling over the internet or stored on the platform.

Neuroimaging workflow in the cloud: standardizing research

Lazovski, N., Ramos, M., Moreno-Dominguez, D., Sato, T., Peeters, T., Prčkovska, V., & Rodrigues, P. (2017). Neuroimaging workflow in the cloud : standardizing research. In OHBM 2017: 23rd Annual Meeting of the Organization for Human Brain Mapping.

Neuroscientists often face a critical problem hindering collaboration and reproducible research: lack of an efficient and standardized way to share data and to share analytic tools. In this work, we propose a web-based cloud system CloudN designed for neuroimaging workflow, enabling storage, quality control, version control, sharing, analysis, and visualization of various aspects of the neuroimaging data.

BrainVis: A cloud-connected 3D exploration and visualization tool for multi-modal neuroimaging data

Prčkovska, V., Peeters, T., Moreno-Dominguez, D., & Rodrigues, P. (2017). BrainVis: A cloud-connected 3D exploration and visualization tool for multi-modal neuroimaging data. In OHBM 2017: 23rd Annual Meeting of the Organization for Human Brain Mapping.
in ISMRM 2017: 25th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

There is a lack of standardization in the current neuroimaging viewers, with various file types and data structures. We developed BrainVis to alleviate these problems: it is a fully interactive 3D viewer, where multiple modalities can be shown simultaneously. Advanced tools are provided such as surface rendering and interactive exploration of tractography streamlines. Seamless integration with a cloud-system provides transparent fetching and processing of data.

Wired minds: The neural underpinnings of the entrepreneurial brain

Rodrigues, P. R., Moreno-Dominguez, D., Ramos, M., Villoslada, P., Gallardo-pujol, D., & Prčkovska, V. (2017). minds: The neural underpinning of the entrepreneurial brain.
In OHBM 2017: 23rd Annual Meeting of the Organization for Human Brain Mapping.
in ISMRM 2017: 25th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

Neural underpinnings of entrepreneurship are not well understood yet. Recent publications suggest that the behavior of psychopaths and entrepreneurs is not very different. This study aims to take an overall view of personality traits typically associated with entrepreneurship and link them to connectivity indices and cortical brain measurements.

(free)Surfing ANTs: a comparative study

Puch, S., Rodrigues, P., Moreno-Dominguez, D., Ramos, M., & Prčkovska, V. (2017). (free)Surfing ANTs: a comparative study. In ISMRM 2017: 25th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

In this work, we analyze the reproducibility and repeatability of two common brain segmentation tools: FreeSurfer and ANTs, and examine their differences for various brain structures.

Multiple sclerosis lesion segmentation using deep learning

Kazancli, E. (2017). Multiple Sclerosis Lesion Segmentation using Deep Learning. Universitat Politecnica de Catalunya.

In this thesis, we evaluate novel approaches to segment Multiple Sclerosis lesions from MR scans based on Convolutional Neural Networks.

2016

Wired minds: How personality traits can predict entrepreneurs’ brains

Ledezma-Haight, R., Ramos, M., Prčkovska, V., Rodrigues, P., & Gallardo-Pujol, D. (2016). Wired minds: How personality traits can predict entrepreneurs’ brains. Personality and Individual Differences101, 493.

Very little is known on how the entrepreneurial brain works and what are the differences. This work aims to take an overall view of the traits found in an entrepreneur (determined by psychometric assessments) and compare these to connectivity levels and volumes in certain areas of the brain.

The potential impact of health IT companies on healthcare industry

Alkin, A. (2016). The potential impact of health IT companies on healthcare industry – based on QMENTA (former Mint Labs).

The aim of this thesis is to understand the healthcare industry and to figure out if advanced AI-powered software companies such as QMENTA can have a lasting and disruptive effect on the healthcare and pharmaceutical industry value chain with their technologies.

Context-based enhancement of diffusion-weighted images for tractography in multiple sclerosis

Abella, A. (2016). Context-based enhancement of diffusion-weighted images for tractography in multiple sclerosis. MSc in Bioinformatics for Health Sciences. Universitat Pompeu Fabra.

In this study, we investigated the effect of context-based enhancements on dMRI in the case of MS patients, in order to test whether this type of processing can lead to a better tractography. This work showed that contextual-based enhancement can indeed provide a better estimation of the optic radiations, especially in those cases with a high lesion load.

Characterizing functional connectivity during rest in multiple sclerosis patients versus healthy volunteers using independent component analysis.

Palacio, L. (2016). Characterizing functional connectivity during rest in multiple sclerosis patients versus healthy volunteers using independent component analysis. Universitat Pompeu Fabra.

In order to study the effects of multiple sclerosis on the functional connectivity of the brain, we applied a numerical method known as independent component analysis (ICA) and implemented a web user interface to allow the user to manually classify all the independent components for a given subject. We did not find any significant functional connectivity differences between MS patients and healthy volunteers.

2015

Contextual Diffusion Image Post-processing Aids Clinical Applications

Prčkovska, V., Andorrà, M., Villoslada, P., Martinez-Heras, E., Duits, R., Fortin, D., … Descoteaux, M. (2015). Contextual Diffusion Image Post-processing Aids Clinical Applications. Mathematics and Visualization, 40(1), 353–377.

In this paper, we present a possibility in enabling HARDI tractography on the data acquired under limited diffusion tensor imaging conditions. We enhance local features from the tensor field taking ‘context’ information into account. Moreover, we demonstrate the potential of the contextual processing techniques in two important clinical applications: enhancing the streamlines in data acquired from patients with Multiple Sclerosis (MS) and pre-surgical planning for tumor resection

Reproducibility of the Structural Connectome Reconstruction across Diffusion Methods

Prčkovska, V., Rodrigues, P., Puigdellivol Sanchez, A., Ramos, M., Andorra, M., Martinez-Heras, E., … Villoslada, P. (2015). Reproducibility of the Structural Connectome Reconstruction across Diffusion Methods. Journal of Neuroimaging, 26(1), 46–57.

In this work, we evaluated the reproducibility of structural connectome techniques on test-retest and longitudinal data from 22 healthy volunteers. We compared connectivity matrices and tract reconstructions obtained with the most typical acquisition schemes used in clinical application.

The effect of the resampling in DTI tractograms

Ramos, M., Serret, C., Tudela, R., Rodrigues, P., Falcón, C., Soria, G., & Prchkovska, V. (2015). The effect of the resampling in DTI tractograms. ISMRM 2015: 6th Annual Meeting of the Italian Chapter of the International Society for Magnetic Resonance in Medicine.

In this work, we investigate the effect of 3 interpolation techniques (trilinear, nearest neighbor and Fischer’s Bresenham interpolation) when resampling to an isotropic resolution on the estimation of the derived DTI scalar maps and reconstructed fibers on “in-vivo” mice data. Results show significant changes in FA values along fibers.

What does it take to build a qMRI laboratory?

Rodrigues, P. (2015). What does it take to build a qMRI laboratory? ISMRM 2015: 6th Annual Meeting of the Italian Chapter of the International Society for Magnetic Resonance in Medicine.

An overview through our journey building QMENTA from an idea to an award-winning start-up, and the vision and goals behind it.

Web-cloud platform for storing, processing and analyzing multi-modal neuroimaging data

Lazovski, N., Ramos, M., Rodrigues, P. (2015). Web-cloud platform for storing, processing and analyzing multi-modal neuroimaging data. ISMRM 2015: 6th Annual Meeting of the Italian Chapter of the International Society for Magnetic Resonance in Medicine.

In this work, we present one of our first designs for the QMENTA web-based cloud system used to store, process, analyze, and visualize aspects of the neuroimaging muli-modal data.

Improved analysis framework for MS connectomes

Andorra, M., Ramos, M., Martinez-Heras, E., Lampert, E., Rodrigues, P., Villoslada, P., Prchkovska, V. (2015). Improved analysis framework for MS connectomes. ISMRM 2015: 6th Annual Meeting of the Italian Chapter of the International Society for Magnetic Resonance in Medicine.

In this work, we found that HARDI acquisition showed the most balanced trade-off between high reproducibility of the connectome, higher rate of path detection and of fanning fibers, and intermediate acquisition times (10-15 minutes), although at the cost of a higher appearance of aberrant fibers.

2014

Reproducibility of the structural connectome and other open challenges

Rodrigues, P., Prats-Galino, A., Gallardo-Pujol, D., Villoslada, P., Falcon, C., & Prčkovska, V. (2014). Reproducibility of the structural connectome and other open challenges. In ISMRM 2014: 22th Annual Meeting of the International Society for Magnetic Resonance in Medicine.

In this work we address the reproducibility of the structural connectome if acquired under different q-space sampling conditions, and in the same and different scanning session. We also address several caveats that should be taken with care when performing this type of analysis approaches with DWI data.

2013

Evaluating Structural Connectomics: the effect of the cortical parcellation scheme

Rodrigues, P., Prats-Galino, A., Gallardo-Pujol, D., Villoslada, P., Prčkovska, V., & Falcon, C. (2013). Evaluating Structural Connectomics: the effect of the cortical parcellation scheme. Frontiers in Neuroinformatics, 7(May 2015), 29–32.

In this work, we evaluated the information difference contained in the structural connectomes constructed from the same subject, at different levels of the cortical parcellation, scanned with different dMRI acquisition techniques (DTI, HARDI and DSI).

Evaluating Structural Connectomics in Relation to Different Q-space Sampling Techniques

Rodrigues, P., Prats-Galino, A., Gallardo-Pujol, D., Villoslada, P., Falcon, C., & Prčkovska, V. (2013). Evaluating structural connectomics in relation to different Q-space sampling techniques. Lecture Notes in Computer Science (Including Lecture Notes in Bioinformatics), 8149 LNCS(PART 1), 671–678.

In this work, we evaluate the structural connectome by analyzing and comparing graph-based measures on real data acquired using the three most important Diffusion Weighted Imaging techniques: DTI, HARDI and DSI.

Diffusion Tensor Imaging study for Multiple Sclerosis in the Optic Radiation

Tehrani, M. A., Martinez-Heras, E., Rodrigues, P., Gabilondo, I., Falcon, C., Villoslada, P., & Prckovska, V. (2013). Diffusion Tensor Imaging study for Multiple Sclerosis in the Optic Radiation. In ISMRM Workshop on Multiple Sclerosis as a Whole-Brain Disease.

Diffusion tensor imaging (DTI) enables the reconstruction of fiber bundles and white matter tracts and previous findings have demonstrated that it is sensitive to the evolution of tissue damage within Multiple Sclerosis lesions. We investigate the sensitivity power of scalar indices derived from DTI, for detecting lesions present in the white matter of Multiple Sclerosis patients.